# limitations under the License.
# ==============================================================================

import tempfile
from mlir import ir
from mlir.dialects import stablehlo
import tensorflow as tf
from tensorflow.compiler.mlir.tensorflow_to_stablehlo.python import pywrap_tensorflow_to_stablehlo as tensorflow_to_stablehlo
from tensorflow.python.platform import test

	if err == nil {
		t.Fatalf("should returns error but got nil")
	}
}

func TestReturningWithNullToZeroValues(t *testing.T) {
	dialect := DB.Dialector.Name()
	switch dialect {
	case "mysql", "sqlserver":
		// these dialects do not support the "returning" clause
		return
	default:
		// This user struct will leverage the existing users table, but override
		// the Name field to default to null.

}
```

## TensorFlow Executor Dialect

The `tf_executor` dialect is intended to model the current TensorFlow executor
semantics and (when combined with the `tf` dialect) can represent arbitrary
TensorFlow 1.x and 2.x graphs. As such it follows the executor model, including
deadness propagation, concurrent semantics, and control dependencies. The
`tf_executor` dialect defines two dialect-specific types:

namespace mlir {
class ShapedType;
}  // namespace mlir

namespace tensorflow {

using tsl::StatusOr;

// Add custom op prefix for TensorFlow dialects.
Status AddTensorFlowOpPrefix(std::string);

// Maps an MLIR op name in the TensorFlow dialect or the TensorFlow control
// dialect back into a TensorFlow valid op name.
absl::StatusOr<llvm::StringRef> GetTensorFlowOpName(llvm::StringRef);

# Description:
#   TensorFlow/TensorFlow Lite/XLA MLIR dialects and tools.

load(
    "//tensorflow:tensorflow.bzl",
    "tf_cc_binary",
    "tf_cc_test",
)
load("//tensorflow:tensorflow.default.bzl", "filegroup")
load("//tensorflow/core/platform:rules_cc.bzl", "cc_library")

package(
    # copybara:uncomment default_applicable_licenses = ["//tensorflow:license"],
    default_visibility = ["//visibility:public"],
    licenses = ["notice"],

class ConvertTfQuantToMhloIntTest : public Test {
 protected:
  void SetUp() override {
    DialectRegistry dialects;
    dialects.insert<TF::TensorFlowDialect, func::FuncDialect, chlo::ChloDialect,
                    mhlo::MhloDialect, quant::QuantizationDialect>();
    ctx_ = std::make_unique<MLIRContext>(dialects);
    ctx_->loadAllAvailableDialects();

    // Create a CPU client with 1 device.
    TF_ASSERT_OK_AND_ASSIGN(

#include "llvm/Support/MemoryBuffer.h"
#include "llvm/Support/SMLoc.h"
#include "llvm/Support/SourceMgr.h"
#include "mlir/Dialect/Arith/IR/Arith.h"  // from @llvm-project
#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/Dialect/SCF/IR/SCF.h"  // from @llvm-project
#include "mlir/Dialect/Shape/IR/Shape.h"  // from @llvm-project
#include "mlir/IR/Builders.h"  // from @llvm-project

}

func TestToSQL(t *testing.T) {
	// By default DB.DryRun should false
	if DB.DryRun {
		t.Fatal("Failed expect DB.DryRun to be false")
	}

	if DB.Dialector.Name() == "sqlserver" {
		t.Skip("Skip SQL Server for this test, because it too difference with other dialects.")
	}

	date, _ := time.ParseInLocation("2006-01-02", "2021-10-18", time.Local)

	// find
	sql := DB.ToSQL(func(tx *gorm.DB) *gorm.DB {

  // after the legalize below, for now it needs to be below the above passes
  // that work on TF dialect and before inliner so that the function calls in
  // body and cond are inlined for optimization.
  pass_manager->addPass(mlir::TFL::CreateLegalizeTFWhilePass());

  // Add function inlining pass. Both TF and TFLite dialects are opted into
  // function inliner interface.
  pass_manager->addPass(mlir::createInlinerPass());

"""Base test class for quantize_model Tests."""

from typing import List, Mapping, Optional, Sequence, Tuple

from absl.testing import parameterized
from mlir import ir
from mlir.dialects import stablehlo as stablehlo_dialect
import numpy as np
import tensorflow  # pylint: disable=unused-import

from tensorflow.compiler.mlir.stablehlo import stablehlo
from tensorflow.python.eager import def_function